STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Algorithms Design and Analysis Review Review Article

Enhancing Smart Grid Resilience Through AI-Based Fault Classification

  1. Alok Prasad Department Electrical Engineering, Bansal Institute of Engineering and Technology, Lucknow
  2. Alok Kumar Department Electrical Engineering, Bansal Institute of Engineering and Technology, Lucknow

Abstract

Traditional power grids can be developed into smart grids, and they are comprised of the latest information and communication technologies (ICTs), which are based on establishing the relationship between the conventional electricity systems along with the usage of smart meters and distributed generation. This dynamic improves energy efficiency and the integration of renewables. Well, the dynamic and reversible power injection from Distributed Energy Resources (DERs) creates substantial operational problems. These features include very variable fault currents, more complex patterns of power flow, and high levels of harmonic distortion. This volatility makes traditional, static-level relay technology increasingly ineffective because it is not fast or flexible enough to accurately pick up faults in today’s environment. This paper provides a rapid and flexible fault detection and classification solution for Software-Defined Networks (SDNs) in response to this demand. We present a novel model-based artificial intelligence (AI)-enabled data-driven technique for accurate classification of different fault types. The approach includes safe voltage and current high-fidelity data acquisition on the fly with advanced signal processing to extract discriminatory features, characterizing individual fault signals. Then, the features of interest are used to train a strong Random Forest (RF) classifier. The proposed AI model shows excellent performance with an exceptional accuracy of 98.04% in categorizing the fault conditions. Importantly, it can decide in less than 10 milliseconds. The speed and accuracy of this process confirm that AI is a key enabler to provide significant improvements in the reliability and operational responsiveness of smart grids during transient fault conditions.

Keywords

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